A large
number of U.S. laws prohibit disability-based discrimination. At the federal level, examples are the
Americans with Disabilities Act (ADA), the Fair Housing Act, the Rehabilitation
Act of 1973, Section 1557 of the Affordable Care Act, and the Genetic
Information Nondiscrimination Act. In
addition, almost all of the states have adopted disability discrimination laws. This might lead to the conclusion that we
enjoy comprehensive legislative protection against discrimination associated
with health status. Unfortunately, in
the era of big data and artificial intelligence (AI) that is no longer true.

The problem
is that the laws protect individuals based on their present or past health
conditions and do not reach discrimination based on predictions of future
medical ailments. The ADA, for example,
defines disability as follows: a) a physical or mental impairment that
substantially limits a major life activity, b) a record of such an impairment,
or c) being regarded as having such an impairment. This language focuses only on employers’
perceptions concerning workers’ current or past health status.

Modern
technology, however, provides us with powerful predictive capabilities. Using available data, AI can generate valuable
new information about individuals, including predictions of their future health
problems. AI capabilities are available
not only to medical experts, but also to employers, insurers, lenders, and
others who have economic agendas that may not align with the data subjects’
best interests.

AI can be of
great benefit to patients, health care providers, and other stakeholders. Machine learning algorithms have been used to
predict patients’ risk of heart disease, stroke, and diabetes based on their
electronic health record data. Google has used deep-learning algorithms to
predict heart disease by analyzing photographs of individuals’ retinas. IBM has used AI to model the speech patterns
of high-risk patients who later developed psychosis. In 2016, researchers from
the University of California, Los Angeles announced that they had used data
from the National Health and Nutrition Examination Survey to build a statistical
model to predict prediabetes. Armed with
such means, physicians can identify their at-risk patients and counsel them
about lifestyle changes and other preventive measures. Likewise, employers can use predictive
analytics to more accurately forecast future health insurance costs for
budgetary purposes.

Unfortunately,
however, AI and predictive analytics generally may also be used for
discriminatory purposes. Take employers
as an example. Employers are highly motivated
to hire healthy employees who will not have productivity or absenteeism
problems and will not generate high health insurance costs. The ADA permits employers to conduct
wide-ranging pre-employment examinations. Thus, employers may have individuals’
retinas and speech patterns examined in order to identify desirable and
undesirable job applicants. The ADA
forbids employers from discriminating based on existing or past serious health
problems. But no provision prohibits them from using such data to discriminate
against currently healthy employees who may be at risk of later illnesses and thus
could possibly turn out to have low productivity and high medical costs.

This is
especially problematic because statistical predictions based on AI algorithms
may be wrong. They may be tainted by
inaccurate data inputs or by biases. For
example, a prediction might be based on information contained in an
individual’s electronic health record (EHR).
Yet, unfortunately, these records are often rife with errors that can
skew analysis. Moreover, EHRs are often
designed to maximize charge capture for billing purposes. Reimbursement concerns may therefore drive
EHR coding in ways that bias
statistical predictions. So too,
predictive algorithms themselves may be flawed if they have been trained using
unreliable data. Discrimination based on
AI forecasts, therefore, may not only harm data subjects, it may also be based
on entirely false assumptions.

In the wake
of big data and AI, it is time to revisit the nation’s anti-discrimination
laws. I propose that the laws be amended to protect individuals who are
predicted to develop disabilities in the future.

In the case
of the ADA, the fix would be fairly simple.
The law’s “regarded as” provision currently defines “disability” for
statutory purposes as including “being regarded as having … an impairment.” The language could be revised to provide that
the statute covers “being regarded as having … an impairment or as likely to
develop a physical or mental impairment in the future.” Similar wording could be incorporated into
other anti-discrimination laws.

One might object that the suggested approach
would unacceptably broaden the anti-discrimination mandate because it would potentially
extend to all Americans rather than to a “discrete and insular minority” of
individuals with disabilities. After
all, anyone, including the healthiest of humans, could be found to have signs
that forecast some future frailty.

However, the
ADA’s “regarded as” provision is already far-reaching because any individual
could be wrongly perceived as having a mental or physical impairment. Similarly, Title VII of the Civil Rights Act
of 1964 covers discrimination based on race, color, national origin, sex, and
religion. Given that all individuals
have these attributes (religion includes non-practice of religion), the law
reaches all Americans. Consequently, banning
discrimination rooted in predictive data would not constitute a departure from
other, well-established anti-discrimination mandates.

It is noteworthy
that under the Genetic Information Nondiscrimination Act, employers and health
insurers are already prohibited from discriminating based on one type of
predictive data: genetic information.
Genetic information is off-limits not only insofar as it can reveal what
conditions individuals presently have, but also with respects to its ability to
identify perfectly healthy people’s vulnerabilities to a myriad of diseases in
the future.

In the
contemporary world it makes little sense to outlaw discrimination based on
genetic information but not discrimination based on AI algorithms with powerful
predictive capabilities. The proposed
change would render the ADA and other disability discrimination provisions more
consistent with GINA’s prudent approach.

As is often
the case, technology has outpaced the law in the areas of big data and AI. It is time to implement a measured and needed
statutory response to new data-driven discrimination threats.

Sharona Hoffman is Edgar A. Hahn Professor of Law, Professor of Bioethics, and Co-Director of the Law-Medicine Center, Case Western Reserve University School of Law. You can reach her by e-mail at sharona.hoffman at case.edu.